A new web-based system for collecting and storing phenotypes makes it easier to find commonalities between rare genetic disorders. Learn more...

At the Baylor-Johns Hopkins Center for Mendelian Genetics, researchers who are
trying to pin down the genetic causes of rare disorders have a biweekly
meeting to discuss new cases. Often, they’ll recall details of previous
patients with similar symptoms that might inform their next steps.
Connecting the dots between old and new cases mean not only remembering the
patients but also sorting through hard-to-search records.

A new computer system lets scientists quickly pull information from a database of patients with rare disorders, searching by phenotypic features, keywords, or genetic mutations. Source: Bill Branson, NIH

But now, a new computer system developed by the center lets scientists quickly
pull information from a database of patients with rare disorders, searching
by phenotypic features, keywords, or genetic mutations. The program, called
PhenoDB, aims to provide a better organization system for phenotype data not
only for researchers at the center but also other geneticists around the
world.

“We wanted the ability for someone to give a head to toe description of a
patient, as deep as they knew, in a way that would then be searchable,”
explained Ada Hamosh, the clinical director of the McKusick-Nathans
Institute of Genetics Medicine at Johns Hopkins School of Medicine and first
author of a new paper describing the features of the tool, published online
this month in the journal Human Mutation (1). “There are no other
tools that allow you to collect standardized phenotypic information,” she
said.

PhenoDB is based on a set of 2900 clinical terms that describe phenotypes
exhibited by a patient. They are grouped into categories so a researcher can
easily zoom into the correct set of terms—if they check a box indicating
abnormalities in the respiratory system, for example, a new set of
phenotypic categories appear, allowing the researcher to narrow in on the
symptoms.

Once they’ve gotten to the most specific phenotype, additional details can be
added to a text box. Additional anonymized patient information includes
family history information, a record of patient consent forms, and details
on genetic tests conducted.

To test PhenoDB out, Hamosh and colleagues at Johns Hopkins and Baylor added
572 families and five research cohorts to the database and then used it for
a nine month trial period. The database worked smoothly, and now they are
planning to add new features, like a genetic analysis module and better
pedigree mapping.

“Now, any researcher anywhere can take this package and use it,” said Hamosh.
“To customize it is trivial, and to learn to use it is easy.”

Each institution or researcher using the program will only have access to
their own data, but Hamosh said that in the future the database could be
combined with electronic health records in a wider patient setting, allowing
researchers to study phenotypes across large populations. And PhenoDB could
someday contain information that can help make clinical decision making even
easier—recommending tests or drugs based on a patient entry.

For now, Hamosh said, “the takeaway message is that PhenoDB is a fully
integrated research tool to collect phenotypic data, genetic data, and
family history data for any research project that uses whole exomes or full
genomes.”